Memristor-based Neural Networks Research Articles

Event-based nonfragile state estimation for memristive neural networks with multiple time-delays and sensor saturations

This article investigates the issue of nonfragile state estimation (SE) for memristor-based neural networks (MNNs) with leakage delay and proportional delay. In actual engineering, a multitude of usefulness data are transmitted to the estimator through the networks, which stress the burden on communication bandwidth. A dynamic event-triggered mechanism (DETM) that relies on incomplete measurements is utilised to select valuable data. A novel delay-dependent criterion for the existence of the event-based state estimator is derived in terms of a convex optimisation problem by means of the Lyapunov theory and some integer inequalities technique. In the end, two numerical simulations are shown to illustrate the validity of the proposed theoretical methods.

Synchronization of Delayed Memristor-Based Neural Networks via Pinning Control With Local Information.

In this article, a novel pinning control method, only requiring information from partial nodes, is developed to synchronize drive-response memristor-based neural networks (MNNs) with time delay. An improved mathematical model of MNNs is established to describe the dynamic behaviors of MNNs accurately. In the existing literature, pinning controllers for synchronization of drive-response systems were designed based on information of all nodes, but in some specific situations, the control gains may be very large and challenging to realize in practice. To overcome this problem, a novel pinning control policy is developed to achieve synchronization of delayed MNNs, which depends only on local information of MNNs, for reducing communication and calculation burdens. Furthermore, sufficient conditions for synchronization of delayed MNNs are provided. Finally, numerical simulation and comparative experiments are conducted to verify the effectiveness and superiority of the proposed pinning control method.

A memristor-based magnetized Hopfield neural network for hidden scroll chaotic attractors and control analysis with reduced input

A memristor-based magnetized Hopfield neural network for hidden scroll chaotic attractors and control analysis with reduced input

Analysis of the dynamical behavior of discrete memristor-coupled scale-free neural networks

The synchronization of neural networks is crucial for neural information processing and represents a key feature of various functional brain diseases. Memristors are ideal electronic components for mimicking biological synapses, among which discrete memristors have the advantage of fast computing speed and are often used in memristor-based neural networks. For these reasons, this paper proposes a novel discrete memristor-coupled Scale-Free neural network (DMSNN). Phase diagrams and time series of membrane potential are employed to analyze the firing pattern coexistence of individual neurons in the network. Furthermore, Spatiotemporal patterns, heat maps of the Spearman correlation coefficient matrix and the values of neuron membrane potential at a particular time point are adopted to declare the spatio-temporal dynamics of the complex neural network, encompassing asynchronization, chimeric state, synchronization and synchronization transition. The study also identifies the phenomenon of topology-induced coexistence and elucidates the underlying reasons for the emergence of chimeric states in the DMSNN as the coupling strength increases. Finally, a hardware implementation platform is constructed using a highly integrated SSD202 processor to validate the accuracy of the DMSNN. The results are consistent with the numerical simulations.

Memristor-Based Neural Network Circuit of Operant Conditioning With Bridging and Conditional Reinforcement

Memristor-Based Neural Network Circuit of Operant Conditioning With Bridging and Conditional Reinforcement

Black phosphorus quantum dots functionalized with photochromic poly(vinylspiropyran)-grafted polydopamine for transient digital-type memristors

Abstract The covalent functionalization of black phosphorus quantum dots (BPQDs) with organic species or polymers will inevitably change or damage their electronic structure and intrinsic structure. To address this problem and explore the application of BPQDs in transient digital-type memristors, a polydopamine (PDA) thin film is first synthesized in situ onto the surface of BPQDs to produce a donor–acceptor-type BPQDs@PDA composite that is directly used to react with 2-bromoisobutyryl bromide to give BPQDs@PDA-Br. By using BPQDs@PDA-Br as an atom transfer radical polymerization agent, a large number of polyvinylspiropyran (PSP) chains are in situ grown from the PDA surface to yield BPQDs@PDA-PSP. Upon ultraviolet (UV)–visible light illumination, the 2 isomers of the spiropyran (ring-closed spiropyran form and ring-opened merocyanine) in the PSP moieties will interconvert into each other rapidly. As expected, the as-fabricated indium tin oxide (ITO)/BPQDs@PDA-PSP/ITO device exhibits typical nonvolatile digital-type memristive performance under visible irradiation, with a small turn-on voltage of −1.52 V, a turn-off voltage of +1.16 V, and an ON/OFF ratio current ratio of 1.02 × 104. Upon UV illumination, the information stored in the device is quickly and completely erased within 6 s. By utilizing a simple memristor-based convolutional neural network, one can easily realize handwritten digit recognition. After 10 epochs of training, numeral recognition accuracy can reach up to 96.21%.

Resistive switching and synaptic characteristics of Hf-doped ZnO sandwiched between HfO2-based memristors for neuromorphic computing

With the rise of neuromorphic computing, memristors have attracted much attention as a device with potential synaptic properties. In this paper, we investigated the embedding of Hf-doped ZnO (Hf:ZnO) layers with different doping concentrations in HfO2-based memristors to design HfO2/(0 %-10 %)Hf:ZnO/HfO2 tri-layer structured memristors for resistive switching (RS) and synaptic characteristics. Utilizing first-principles calculations, we integrated assessments of both oxygen vacancy migration capability and material conductivity to evaluate the rationale behind our design. The experimental results show that the HfO2/(5 %)Hf:ZnO/HfO2 tri-layer structured memristor presents the best RS performance, with a storage window of 103. We analyze the impact of insertion layers with varying doping concentrations on the RS performance of the devices and elucidate the RS mechanism. In addition, we investigated the synaptic properties of HfO2/(5 %)Hf:ZnO/HfO2 tri-layer structured memristor, and effectively simulated synaptic plasticity akin to the synaptic behavior found in biological neurons. Finally, we also constructed a memristor-based Spiking Neural Network for recognizing the Mixed National Institute of Standards and Technology handwritten digit dataset, achieving a recognition accuracy of 82 %. This comprehensive study shows that the insertion of (5 %)Hf:ZnO into HfO2-based memristors holds significant promise for upcoming applications in the field of neuromorphic computing.

Memristor-based Bayesian spiking neural network for IBD diagnosis

Traditional deep learning methods, such as deep neural networks, have achieved remarkable success in image processing, natural language processing, graph computation, and other fields. However, in the diagnosis of Inflammatory Bowel Disease (IBD), conventional deep learning methods are highly susceptible to generating overconfident results, which can lead to great loss of life and property if it results in misdiagnosis of the patient. In this work, we propose a memristor-based IBD diagnostic system inspired by the idea of variational inference based on Bayes' law. The memristors in our system are used for both synaptic weight storage and entropy sources so that an efficient Bayesian inference system can be realized. Our memristor-based true random number generator can produce probability-tunable bitstreams required for Bayesian inference with a mean-square error that is reduced by 10.9 × than that of a conventional pseudo-random generator. The proposed system consists of a coarse-grained and a fined-grained spiking neural network, and an optimized strategy that trims the neuron's threshold is proposed for maintaining the appropriate model sparsity and reducing information loss. With Bayesian inference, our work achieves lower diagnostic error rates, which are reduced by 10.79 % and 0.49 % for the coarse-grained and fine-grained networks, respectively. The proposed system is easy to hardware implement and presents a novel solution for community-wide IBD diagnosis.

Quantized Nonfragile State Estimation of Memristor-Based Fractional-Order Neural Networks with Hybrid Time Delays Subject to Sensor Saturations

Quantized Nonfragile State Estimation of Memristor-Based Fractional-Order Neural Networks with Hybrid Time Delays Subject to Sensor Saturations

Quasi-synchronization of neural networks via non-fragile impulsive control: Multi-layer and memristor-based

In this paper, the quasi-synchronization for memristor-based multi-layer neural networks is solved, where each layer possesses a unique topology. The focus is on the incorporation of proportional delay, which represents an exceptional unbounded delay. To enhance the network’s robustness against external interferences and minimize control expenses, the non-fragile impulsive control is devised. By utilizing the impulsive comparison principle and interval matrix method, sufficient conditions for achieving quasi-synchronization are derived under discontinuous activation function. Moreover, the error bounds are estimated for synchronizing and desynchronizing impulses. The correctness of the theoretical results is finally testified by numerical examples.

Memristor-Based Neural Network Circuit of Associative Memory With Occasion Setting

Memristor-Based Neural Network Circuit of Associative Memory With Occasion Setting

Exponential Synchronization of Stochastic Time-delayed Memristor-based Neural Networks via Pinning Impulsive Control

Exponential Synchronization of Stochastic Time-delayed Memristor-based Neural Networks via Pinning Impulsive Control

A memristive all-inclusive hypernetwork for parallel analog deployment of full search space architectures

In recent years, there has been a significant advancement in memristor-based neural networks, positioning them as a pivotal processing-in-memory deployment architecture for a wide array of deep learning applications. Within this realm of progress, the emerging parallel analog memristive platforms are prominent for their ability to generate multiple feature maps in a single processing cycle. However, a notable limitation is that they are specifically tailored for neural networks with fixed structures. As an orthogonal direction, recent research reveals that neural architecture should be specialized for tasks and deployment platforms. Building upon this, the neural architecture search (NAS) methods effectively explore promising architectures in a large design space. However, these NAS-based architectures are generally heterogeneous and diversified, making it challenging for deployment on current single-prototype, customized, parallel analog memristive hardware circuits. Therefore, investigating memristive analog deployment that overrides the full search space is a promising and challenging problem. Inspired by this, and beginning with the DARTS search space, we study the memristive hardware design of primitive operations and propose the memristive all-inclusive hypernetwork that covers 2×1025 network architectures. Our computational simulation results on 3 representative architectures (DARTS-V1, DARTS-V2, PDARTS) show that our memristive all-inclusive hypernetwork achieves promising results on the CIFAR10 dataset (89.2% of PDARTS with 8-bit quantization precision), and is compatible with all architectures in the DARTS full-space. The hardware performance simulation indicates that the memristive all-inclusive hypernetwork costs slightly more resource consumption (nearly the same in power, 22%∼25% increase in Latency, 1.5× in Area) relative to the individual deployment, which is reasonable and may reach a tolerable trade-off deployment scheme for industrial scenarios.

Memristor-Based Conditioned Inhibition Neural Network Circuit With Blocking Generalization and Differentiation

Memristor-Based Conditioned Inhibition Neural Network Circuit With Blocking Generalization and Differentiation

Observer-based resilient dissipativity control for discrete-time memristor-based neural networks with unbounded or bounded time-varying delays

This work focuses on the issue of observer-based resilient dissipativity control of discrete-time memristor-based neural networks (DTMBNNs) with unbounded or bounded time-varying delays. Firstly, the Luenberger observer is designed, and additionally based on the observed states, the observer-based resilient controller is proposed. An augmented system is presented by considering both the error system and the DTMBNNs with the controller. Secondly, a novel sufficient extended exponential dissipativity condition is obtained for the augmented system with unbounded time-varying delays by proposing a system solutions-based estimation approach. This method is based on system solutions and without constructing any Lyapunov–Krasovskii functionals (LKF), thereby reducing the complexity of theoretical derivation and computational workload. In addition, an algorithm is proposed to solve the nonlinear inequalities in the sufficient condition. Thirdly, the sufficient extended exponential dissipativity condition for the augmented system with bounded time-varying delays is also obtained. Finally, the effectiveness of the theoretical results is illustrated through two simulation examples.

New results on synchronization control of memristor-based quaternion-valued fuzzy neural networks with delayed impulses

A model of memristor-based quaternion-valued fuzzy neural networks (MQVFNNs) with delayed impulses and time-varying delays is established, and the synchronization issue is investigated in this paper. Firstly, to avoid non-commutativity, the MQVFNNs are decomposed into real-valued delayed impulsive neural networks. Secondly, a novel lemma for delayed impulsive system is introduced based on Halanay differential inequality. Then, by employing the theory of differential inclusion and the proposed lemma, some sufficient conditions are deduced to guarantee the global exponential synchronization of memristive quaternion-valued fuzzy neural networks under a simple delayed nonlinear control scheme. Finally, a numerical simulation is given to demonstrate the effectiveness of our theoretical results.

A Memristor Neural Network Based on Simple Logarithmic-Sigmoidal Transfer Function with MOS Transistors

Memristors are state-of-the-art, nano-sized, two-terminal, passive electronic elements with very good switching and memory characteristics. Owing to their very low power usage and a good compatibility to the existing CMOS ultra-high-density integrated circuits and chips, they are potentially applicable in artificial and spiking neural networks, memory arrays, and many other devices and circuits for artificial intelligence. In this paper, a complete electronic realization of an analog circuit model of the modified neural net with memristor-based synapses and transfer function with memristors and MOS transistors in LTSPICE is offered. Each synaptic weight is realized by only one memristor, providing enormously reduced circuit complexity. The summing and scaling implementation is founded on op-amps and memristors. The logarithmic-sigmoidal activation function is based on a simple scheme with MOS transistors and memristors. The functioning of the suggested memristor-based neural network for pulse input signals is evaluated both analytically in MATLAB-SIMULINK and in the LTSPICE environment. The obtained results are compared one to another and are successfully verified. The realized memristor-based neural network is an important step towards the forthcoming design of complex memristor-based neural networks for artificial intelligence, for implementation in very high-density integrated circuits and chips.

Powering AI at the edge: A robust, memristor-based binarized neural network with near-memory computing and miniaturized solar cell

Memristor-based neural networks provide an exceptional energy-efficient platform for artificial intelligence (AI), presenting the possibility of self-powered operation when paired with energy harvesters. However, most memristor-based networks rely on analog in-memory computing, necessitating a stable and precise power supply, which is incompatible with the inherently unstable and unreliable energy harvesters. In this work, we fabricated a robust binarized neural network comprising 32,768 memristors, powered by a miniature wide-bandgap solar cell optimized for edge applications. Our circuit employs a resilient digital near-memory computing approach, featuring complementarily programmed memristors and logic-in-sense-amplifier. This design eliminates the need for compensation or calibration, operating effectively under diverse conditions. Under high illumination, the circuit achieves inference performance comparable to that of a lab bench power supply. In low illumination scenarios, it remains functional with slightly reduced accuracy, seamlessly transitioning to an approximate computing mode. Through image classification neural network simulations, we demonstrate that misclassified images under low illumination are primarily difficult-to-classify cases. Our approach lays the groundwork for self-powered AI and the creation of intelligent sensors for various applications in health, safety, and environment monitoring.

Exponential synchronization of quaternion-valued memristor-based Cohen-Grossberg neural networks with time-varying delays: norm method.

In this paper, the exponential synchronization of quaternion-valued memristor-based Cohen-Grossberg neural networks with time-varying delays is discussed. By using the differential inclusion theory and the set-valued map theory, the discontinuous quaternion-valued memristor-based Cohen-Grossberg neural networks are transformed into an uncertain system with interval parameters. A novel controller is designed to achieve the control goal. With some inequality techniques, several criteria of exponential synchronization for quaternion-valued memristor-based Cohen-Grossberg neural networks are given. Different from the existing results using decomposition techniques, a direct analytical approach is used to study the synchronization problem by introducing an improved one-norm method. Moreover, the activation function is less restricted and the Lyapunov analysis process is simpler. Finally, a numerical simulation is given to prove the validity of the main results.

Delay-independent control for synchronization of memristor-based BAM neural networks with parameter perturbation and strong mismatch via finite-time technology

This paper mainly studies the synchronization problem of memristor-based bidirectional associative memory neural networks (MBAMNNs) via finite-time technology. Different from the existing neural network dynamic models, the given model in this paper is focused on the impact of parameter perturbation and strong mismatch, where strong mismatch includes parameter mismatch and time-varying delay mismatch. These characteristics can make the model be closer to the actual situation. A delay-independent feedback control scheme, which can stabilize the error system within finite-time regardless of whether the past state is known or not, is designed. It is worth noting that the constant is replaced by a function with the exponential term in the delay-independent controller, which can save the control cost to a certain extent. Based on the integral inequality technique, some sufficient conditions for MBAMNNs to converge to the equilibrium point within finite-time are provided. The validity and correctness of the theoretical results are finally confirmed by numerical simulation.